Similarity of Node2Vec vectors. As a node embedding method [1]}, Node2Vec [2]} proposes to represent each node \(v_s\in \mathcal {G} \)  as a compact vector, which preserves the structural information in the vicinity of \(v_s\) . By this method, given a friendship \((v_s,v_t)\in \mathcal {E} \) , the \({\mathtt {TFC}}\)  can be measured by the cosine similarity or euclidean distance between the Node2Vec vectors of \(v_s\)  and \(v_t\) . Compared with the prior work DeepWalk [3]}, the core contribution of Node2Vec is that it takes two-order random walks as inputs for training. Compared with the RWR, the two-order random walk will not terminate until a pre-defined length limit. Moreover, given the current node \(v_j\) , the target neighbors of \(v_j\)  are first divided into the following three categories: (i) the source neighbor \(v_i\)  that was visited in the previous step, (ii) \(v_k\)  that is the common target neighbor of \(v_i\)  or (iii) otherwise. Then different (resp. same) transition probabilities are assigned to nodes across (resp. inside) each category. At last, the next node of this walk is selected in terms of the biased probability.
